code: def forward(
      self,
      data: Tensor,
      feats: Optional[Tensor],
      mask: Optional[BoolTensor],
  ) -> Tuple[Tensor, Tensor]:
      shape, inter_value, extra_value = self.encoder(data, feats)
      self.shape = shape.detach()
      self.inter_value = inter_value.detach()
      self.extra_value = extra_value.detach()
      interp, extrap = self.kernel(
          shape, inter_value, extra_value, mask
      )
      self.query = self.kernel._query
      self.key = self.kernel._key
      self.inter_score = self.kernel._inter_score
      self.extra_score = self.kernel._extra_score
      interp = self.decoder(interp)
      extrap = self.decoder(extrap)
      return interp, extrap
  
imports: from typing import Optional, List, Tuple, Union
  import numpy
  import torch
  from torch import Tensor, BoolTensor, nn
  from torch.nn import init, functional
  from tslib.nn.transformer import PositionwiseFFN
  from .kernel import AttentionKernel
  from .disc import SimpleDiscriminator
label_desc: Use image node_id 2 for calling the AttentionBlock obj's underlying member method code with example usage: obj.forward(data, feats, mask) and returns: interp,  extrap